@article{ART003380955},
author={Kim, Sung-Yeon},
title={Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading},
journal={Modern English Education},
issn={1598-0782},
year={2026},
volume={27},
pages={396-411}
TY - JOUR
AU - Kim, Sung-Yeon
TI - Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading
JO - Modern English Education
PY - 2026
VL - 27
IS - null
PB - The Modern English Education Society
SP - 396
EP - 411
SN - 1598-0782
AB - This study investigated whether and to what extent EFL college learners and a trained bilingual rater differed in evaluating the adequacy of machine translation (MT) across four text types: English for Specific Purposes (ESP), argumentative (Opinion), expository (Information), and literary (Literature). A mixed-methods design was adopted to examine translation adequacy ratings and learner perceptions of MTassisted reading. At the macro level, the two groups showed close agreement: both rated Opinion and ESP as highly adequate (Rater M = 4.63, 4.55; Student M = 4.29, 4.41) and Literature as the most vulnerable domain (Rater M = 3.64; Student M = 4.06).
Despite this shared rank order, a notable disparity emerged at the micro level. The rater scored Literature more critically owing to its stylistic complexity, whereas the learners rated it more leniently, a pattern that may reflect the surface-level readability of the text rather than the quality of its translation. Survey results also revealed a mismatch between perceived accuracy and actual preference: although ChatGPT was rated as the most accurate tool by 62% of participants, more learners preferred Papago (57.1%) over ChatGPT (42.9%) due to its L1-optimized interface. Furthermore, significant affective trade-offs emerged as the learners viewed MT as a helpful scaffold for reading, while simultaneously holding mixed feelings of trust and skepticism toward MT output. The study concludes with pedagogical implications for developing critical MT literacy in EFL curricula.
KW - Machine translation;translated output;reading text;text type;machine translation adequacy evaluation
DO -
UR -
ER -
Kim, Sung-Yeon. (2026). Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading. Modern English Education, 27, 396-411.
Kim, Sung-Yeon. 2026, "Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading", Modern English Education, vol.27, pp.396-411.
Kim, Sung-Yeon "Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading" Modern English Education 27 pp.396-411 (2026) : 396.
Kim, Sung-Yeon. Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading. 2026; 27 396-411.
Kim, Sung-Yeon. "Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading" Modern English Education 27(2026) : 396-411.
Kim, Sung-Yeon. Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading. Modern English Education, 27, 396-411.
Kim, Sung-Yeon. Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading. Modern English Education. 2026; 27 396-411.
Kim, Sung-Yeon. Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading. 2026; 27 396-411.
Kim, Sung-Yeon. "Learner and trained rater evaluation of machine translation (MT) across text types and learner perceptions of MT-assisted reading" Modern English Education 27(2026) : 396-411.
